WORKED EXAMPLES
Guides
Beginner-friendly guides that connect statistical questions to suitable methods. Follow clear examples, inspect the calculations and learn what each method can and cannot tell you.
TOPIC LIBRARY
Explore statistics and data analysis
Start with a method, an analysis question or a research task.
METHODS
Statistical methods
Choose tests and models by matching the question to the data and study design.
ANALYSIS
Statistical analysis
Prepare, explore and interpret data using transparent steps and examples.
PREDICT CATEGORIES
Classification
Understand binary and multiclass prediction, thresholds and evaluation.
FIND GROUPS
Cluster analysis
Explore unsupervised grouping, distance, scaling and validation.
PATTERNS
Data mining
Learn how to find useful patterns and check them on new data.
RESEARCH
Statistical research
Plan analyses, test research questions and report results responsibly.
NUMERIC EVIDENCE
Quantitative analysis
Turn numeric observations into clear summaries and evidence.
STATISTICS
Regression and model evaluation
Worked examples with real numbers, formulas and honest limitations.
LINEAR REGRESSION
Linear regression worked example
Fit a least-squares line to study hours and exam scores by hand, then compare with the calculator.
Calculator linkedINTERPRETATION
R-squared vs correlation
What each measure reports, when they agree, and why R² is not correlation squared in every context.
Two live toolsPREDICTION ERROR
RMSE vs MAE
A worked example showing how an outlier changes RMSE much more than MAE, and when that matters.
Calculator linkedCLASSIFICATION
When to use logistic regression
Learn what logistic regression predicts, with a small probability example and clear limits.
Python examplePYTHON
Logistic regression with gradient descent
See how probability, log loss and repeated weight updates fit a simple classifier.
Worked codeMODEL CHOICE
Types of regression in machine learning
Compare linear, polynomial, regularized and tree-based regression models.
Model comparisonMULTIPLE OUTCOMES
Multiple dependent variables and one predictor
Learn the name of this model setup and how it differs from multiple regression.
R exampleOUTPUT EXPLAINED
How to read a linear regression summary
Understand coefficients, standard errors, p-values, R-squared and residual checks.
Python exampleNEW EXAMPLES
Recently published guides
New worked examples appear here as they are released.
DATA MINING
Statistical Methods in Data Mining: A Practical Guide
Understand how summaries, classification, clustering and validation fit into a careful data-mining workflow.
Worked exampleCLASSIFICATION
Logistic Regression for Binary Classification: A Worked Example
See how logistic regression turns input features into a probability for one of two classes, and how a threshold changes the decision.
Worked exampleSTATISTICAL ANALYSIS
Statistical Analysis of Survey Data: A Beginner Workflow
Prepare survey data, respect sample weights, summarize responses and report uncertainty with a worked example.
Worked exampleSTATISTICAL METHODS
How to Choose a Statistical Method for Your Data
Choose a statistical method by matching your question, outcome type and study design, with a small worked example.
Worked exampleSOFTWARE TESTING
Saved output comparison
How to compare captured outputs without executing the system under test.
What these guides are
These guides explain the method used by each tool, walk through a concrete numerical or JSON example, and list what the tool cannot decide on its own. They are not research papers and do not claim authority beyond the stated formulas and limitations.